Gas metal arc welding (GMAW) is commonly used to join pieces of metal in high throughput production, for example, in assembly lines employing robotic welders, such as in automotive manufacturing. An electric arc between one or more workpieces and a consumable electrode liquefies the electrode into metal droplets, which are shielded by an inert gas such as argon. These droplets form a weld by penetrating the metal of the workpieces before solidifying.
A variation called pulsed GMAW, which is controlled by pulsing the current and/or voltage of the welding power supply, is particularly preferred in high throughput production because it produces low spattering and good bead finish while generating little heat. These characteristics are essential to avoid heat distortion and residual stress on relatively thin workpieces.
A significant limitation of pulsed GMAW is the complexity of optimizing the power supply parameters in real time in response to changing materials and process variables. In an attempt to deal with this limitation, commercially available GMAW systems employ computerized parameter control in combination with recorded information about materials and ideal process conditions. However, these efforts are limited for two reasons.
Generally, such simple algorithmic control schemes involve understanding of the fundamental relationships between all process variables and the ability to measure and analyze those variables in real time. For GMAW processes, these fundamental relationships are not yet completely understood. Furthermore, were these relationships understood, it would still be expensive and complicated to measure the essential variables and calculate appropriate control actions, all in real time.
For some schemes, the level of understanding and the number of variables that are measured can be reduced by compiling databases of observed variables correlated with optimized control parameters. However, for many commercially important GMAW processes, the number of independent process variables makes this a difficult, time-consuming task. Furthermore, a specific application may introduce a process variable not contemplated during development, rendering the scheme inefficient or unusable in that application.
An attempt to solve the parameter complexity problem proposed to vary a single control variable, the pulse period, based on a single observed variable, the arc light intensity. Generally, the intensity varies with time in relation to the formation of liquid metal droplets at the arc. Because high weld quality is related directly to controlled droplet transfer, this method proposes to improve weld quality by cutting the pulse period short in response to an arc light intensity cutoff. This method, however, has not been successful because of numerous defects. First, the variation of arc light intensity does not always give a definite indication of droplet transfer. Second, many welding conditions of commercial interest do not generate sufficient variation in arc light intensity to reliably trigger a cutoff, for example, carbon steel welding shielded with argon based mixtures with less than 5% of carbon dioxide. Third, control of a GMAW process by varying each instant pulse in real time can lead to over-control of current variations, which results in control oscillation, unstable metal transfer, and poor weld quality.